\(W_\text{Pr}\) : To obtain the Prior Working Experience, we first extract the image embeddings of all training images from the last average pooling layer of ResNet-152. Then, given an input image, we again use the ResNet-152 to obtain the image embedding. At last, we retrieve \(N_\text{K}=100\)  images from the training corpus with the highest cosine similarity to the input image. The reports of the top-\(N_\text{K}\)  retrieved images are returned and encoded as the \(W_\text{Pr} = \lbrace R_1,R_2,\dots ,R_{N_\text{K}}\rbrace  \in \mathbb {R}^{N_\text{K} \times d}\) . In implementations, we use a BERT encoder [1]}, [2]} followed by a max-pooling layer over all output vectors as the report embedding module to get the embedding \(R_i \in \mathbb {R}^{d}\)  of the \(i^\text{th}\)  retrieved report.
